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Register a new userValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries
Valnorm يحدد دلالات للكشف عن تحيزات التكافؤ متسقة عبر اللغات وعدد القرون
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تعلم Word Ageddings تحيزات ضمنية من العظميات اللغوية التي تم التقاطها إحصائيات Word في حدوثها. من خلال تمديد الطرق التي تقيس تحيزات تشبه الإنسان في Word Embeddings، نقدم Valnorm، وهي مهمة وطريقة تقييم جوهرية جديدة لتحديد البعد الافتراضي للتأثير في مجموعات الكلمة المصنوعة من البشرية من علم النفس الاجتماعي. نحن نطبق Valnorm على Adgeddings كلمة ثابتة من سبع لغات (الصينية والإنجليزية والألمانية والبولندية والبرتغالية والإسبانية والتركية) من النص الإنجليزي التاريخي الممتد إلى 200 عام. يحقق Valnorm دقة عالية باستمرار في تحديد تكاليف مجموعات كلمات المجموعة غير التمييزية وغير الاجتماعية. على وجه التحديد، يحقق Valnorm ترابط بيرسون ل R = 0.88 لعشرات الحكم البشري من التكافؤ لمدة 399 كلمة تم جمعها لإنشاء معايير ممتعة باللغة الإنجليزية. على النقيض من ذلك، نقيس القوالب النمطية الجنسانية باستخدام نفس مجموعة من embeddings Word وتجد أن التحيزات الاجتماعية تختلف عبر اللغات. تشير نتائجنا إلى أن جمعيات التكافؤ في كلمات الفريق غير التمييزية غير التمييزية تمثل جمعيات مشتركة على نطاق واسع، بسب سبع لغات وأكثر من 200 عام.
Word embeddings learn implicit biases from linguistic regularities captured by word co-occurrence statistics. By extending methods that quantify human-like biases in word embeddings, we introduce ValNorm, a novel intrinsic evaluation task and method to quantify the valence dimension of affect in human-rated word sets from social psychology. We apply ValNorm on static word embeddings from seven languages (Chinese, English, German, Polish, Portuguese, Spanish, and Turkish) and from historical English text spanning 200 years. ValNorm achieves consistently high accuracy in quantifying the valence of non-discriminatory, non-social group word sets. Specifically, ValNorm achieves a Pearson correlation of r=0.88 for human judgment scores of valence for 399 words collected to establish pleasantness norms in English. In contrast, we measure gender stereotypes using the same set of word embeddings and find that social biases vary across languages. Our results indicate that valence associations of non-discriminatory, non-social group words represent widely-shared associations, in seven languages and over 200 years.
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